Cogitator
Self-hosted runtime for production AI agents
What it does
Open-source, self-hosted alternative to OpenAI Assistants API Run any LLM (Ollama, OpenAI, Anthropic, Google, etc.) with unified TypeScript: • Multi-agent swarms (7 strategies) • Tree of Thoughts reasoning • Time-travel debugging • Self-reflection & learning • Constitutional AI guardrails • Causal reasoning engine • Hybrid memory + vectors • Sandboxed execution • Cost-aware routing • MCP & OpenTelemetry •..and much more! No vendor lock-in. Pure TypeScript.
Does a similar job
all alternatives →- CACogitator – A Python Toolkit for Chain-of-Thought Prompting2025 · github.com · ▲54
Hi everyone, I made an open-source Python toolkit/library, named Cogitator, to make it easier to try and use different chain-of-thought (CoT) reasoning methods. The project is at the beta stage, but it supports using models provided by OpenAI and Ollama. It includes implementations for Cot strategies and frameworks like Self-Consistency, Tree of Thoughts, and Graph of Thoughts. GitHub link of the project: https://github.com/habedi/cogitator
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Hello HN! The day has finally come to stop adding features and start sharing what I've been building the last 5-6 months. It's a bit of CrewAI, OpenDevon, LangFuse/Cloud all in one, providing devs who prefer TypeScript an integrated framework thats provides a lot out of the box to start experimenting and building agents with. It started after peeking at the LangChain docs a few times and never liking the example code. I began experimenting with automating a simple Jira request from the engineering team to add an index to one of our Google Spanner databases (for context I'm the…
Cloudways Managed AI Agents18d ago · cloudways.com · ▲84Skip the setup and run OpenClaw & Hermes, fully managed

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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
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